FLOOD FORECASTING BY USING MACHINE LEARNING

Abstract

Floods are one of the most destructive natural disasters and can cause significant damage to human life, agriculture, infrastructure, and the environment. Accurate and timely flood forecasting can help authorities and communities take preventive measures and reduce the impact of flooding. Traditional flood forecasting methods mainly depend on historical records, hydrological models, rainfall measurements, and manual analysis. These approaches may have limitations when dealing with complex relationships between rainfall, water levels, temperature, and other environmental factors. The Flood Forecasting Using Machine Learning system is designed to predict the possibility or severity of flooding using historical and real-time environmental data. The proposed system collects parameters such as rainfall, river or water level, temperature, humidity, soil moisture, and other relevant features. The collected data is preprocessed and used to train machine-learning models. The proposed system therefore combines environmental data, machine learning, predictive analytics, and earlywarning mechanisms to support flood monitoring and forecasting.